There are so many pose estimation models on imgclsmob repo. I'll collect the model's link and mobile models converted to .tflite format.
| Model Name |
Model Extractor |
TFLite Model Link |
TFLite Model Size |
TFLite Model Input Shape |
TFLite Model Output Shape |
Keras Model Link (.h5) |
Keras Model Size |
| AlphaPose |
Fast-SE-ResNet-101b |
N/A |
238.1 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
211 MB |
| SimplePose |
ResNet-18 |
link |
61.5 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
54.3 MB |
| SimplePose |
ResNet-50b |
link |
135.9 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
120 MB |
| SimplePose |
ResNet-101b |
link |
211.8 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
188 MB |
| SimplePose |
ResNet-152b |
link |
274.3 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
243 MB |
| SimplePose |
ResNet(A)-50b |
link |
136 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
120 MB |
| SimplePose |
ResNet(A)-101b |
link |
211.9 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
188 MB |
| SimplePose |
ResNet(A)-152b |
link |
274.4 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
243 MB |
| SimplePose(Mobile) |
ResNet-18 |
N/A |
51.4 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
45.4 MB |
| SimplePose(Mobile) |
ResNet-50b |
N/A |
102.3 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
90.5 MB |
| SimplePose(Mobile) |
1.0 MobileNet-224 |
N/A |
20.1 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
17.8 MB |
| SimplePose(Mobile) |
1.0 MobileNetV2b-224 |
N/A |
16.4 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
14.6 MB |
| SimplePose(Mobile) |
MobileNetV3 Small 224/1.0 |
N/A |
10.5 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
9.23 MB |
| SimplePose(Mobile) |
MobileNetV3 Large 224/1.0 |
N/A |
19.1 MB |
(1,224,224,3) |
(1,56,56,17) |
link |
16.9 MB |
| Lightweight OpenPose 2D |
MobileNet |
N/A |
16.4 MB |
(1,224,224,3) |
(1,28,28,57) |
link |
14 MB |
| Lightweight OpenPose 3D |
MobileNet |
N/A |
20.4 MB |
(1,224,224,3) |
(1,28,28,114) |
link |
17.5 MB |
| IBPPose |
- |
N/A |
- |
- |
- |
link |
339 MB |
Model Source
https://github.com/osmr/imgclsmob/tree/v0.0.460/tensorflow2#coco-keypoints-detection
TFLite Converting Script
https://github.com/osmr/imgclsmob/blob/bb5532e21ff27689e9f1dc133d86933b04396a1c/examples/convert_tf2_to_tfl.py
Sample Command
python convert_tf2_to_tfl.py \
--model lwopenpose2d_mobilenet_cmupan_coco \
--output-dir /home/outputs/pose/tflite;
There are so many pose estimation models on imgclsmob repo. I'll collect the model's link and mobile models converted to
.tfliteformat..h5)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,56,56,17)(1,224,224,3)(1,28,28,57)(1,224,224,3)(1,28,28,114)Model Source
https://github.com/osmr/imgclsmob/tree/v0.0.460/tensorflow2#coco-keypoints-detection
TFLite Converting Script
https://github.com/osmr/imgclsmob/blob/bb5532e21ff27689e9f1dc133d86933b04396a1c/examples/convert_tf2_to_tfl.py
Sample Command
python convert_tf2_to_tfl.py \ --model lwopenpose2d_mobilenet_cmupan_coco \ --output-dir /home/outputs/pose/tflite;